– A team from the University of Virginia School of Medicine is leveraging the power of quantum computing to gain better insight into genetic diseases with machine learning.
Although quantum computers are still in their infancy, the researchers noted that when they do advance, they could offer computing power on a scale that’s unimaginable on traditional computers.
“We developed and implemented a genetic sample classification algorithm that is fundamental to the field of machine learning on a quantum computer in a very natural way using the inherent strengths of quantum computers,” said Stefan Bekiranov, PhD.
“This is certainly the first published quantum computer study funded by the National Institute of Mental Health and may be the first study using a so-called universal quantum computer funded by the National Institutes of Health.”
Quantum computers can consider significantly more possibilities than traditional computer programs. However, this means that the technology is very technically demanding, and machine learning algorithms have to contain instructions for what to do as well as how to compensate for any errors.
“Our goal was to develop a quantum classifier that we could implement on an actual IBM quantum computer. But the major quantum machine learning papers in the field were highly theoretical and required hardware that didn’t exist. We finally found papers from Dr. Maria Schuld, who is a pioneer in developing implementable, near-term, quantum machine learning algorithms. Our classifier builds on those developed by Dr. Schuld,” Bekiranov said.
“Once we started testing the classifier on the IBM system, we quickly discovered its current limitations and could only implement a vastly oversimplified, or ‘toy,’ problem successfully, for now.”
The algorithm developed by UVA researchers is able to classify genomic data. The tool can tell if a test sample comes from a disease or control sample infinitely faster than a conventional computer.
For example, if they used all four building blocks of DNA (A, G, C, or T) for the classification, a traditional computer would execute three billion operations to classify the sample. The new quantum algorithm would need only 32.
This will help scientists sort through the massive amount of data required for genetic research, and it also demonstrates the potential for the technology to accelerate this type of research.
This project is supported by officials at the National Institutes of Health’s National Institute of Mental Health. The researchers expect that the effort will greatly benefit quantum computing and ultimately human health and well-being.
“Relatively small-scale quantum computers that can solve toy problems are in existence now. The challenges of developing a powerful universal quantum computer are immense. Along with steady progress, it will take multiple scientific breakthroughs,” Bekiranov said.
“But time and again, experimental and theoretical physicists, working together, have risen to these challenges. If and when they develop a powerful universal quantum computer, I believe it will revolutionize computation and be regarded as one of greatest scientific and engineering achievements of humankind.”
In the current COVID-19 pandemic, researchers have leveraged the power of advanced computing to track and mitigate the spread of disease. A team from Penn State’s Institute for Computational and Data Sciences (ICDS) is using an artificial intelligence-powered supercomputer to find solutions for COVID-19 now and going forward.
The goal is to discover possible treatments and therapies for COVID-19, as well as ways to help the world recover socially, economically, and psychologically.
“Our researchers’ response to the COVID-19 pandemic has been phenomenal and the speed and insights that they have shown in creating evidence-based guidance and solutions in support of medical, social and policy responses has been nothing short of inspirational,” said Jenni Evans, professor of meteorology and atmospheric science and ICDS director.
“Our goal is to provide these diverse research teams with the computational tools and support that they need to continue to make advances against this disease, helping society to recover from its ravages and to be more resilient to any future pandemics.”
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